Establishment and Evaluation of a Model for Clinical Feature Selection and Prediction in Gout Patients with Cardiovascular Diseases: A Retrospective Cohort Study

Bingbing Fan¹Yuqing Ye¹Zihan Wang²Yuanyuan Xu³Meishan Lu²Fang Ma^1*

• ¹Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, China
• ²Beijing University of Chinese Medicine, Beijing, Beijing Municipality, China
• ³Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang Province, China

Background: Gout is a chronic inflammatory condition increasingly recognized as a risk factor for cardiovascular events (CVE). Early identification of high-risk individuals is crucial for targeted prevention and management. However, conventional risk stratification approaches often fall short in accuracy and clinical utility. This study aimed to develop and validate a robust, interpretable machine learning (ML)-based model for predicting CVE in patients with gout. Results: Of the 686 patients, 263 experienced cardiovascular events during follow-up (incidence rate: 38.3%). A logistic regression model was constructed based on eight variables selected using the Boruta feature selection algorithm: sex, age, PLT, EOS, LYM, CO2, GLU and APO-B. Among the five models evaluated, the CatBoost classifier achieved the best performance, with the highest area under the ROC curve (AUC) of 0.976 and the recall of 0.971. Furthermore, SHAP (SHapley Additive exPlanations) values were employed to provide both global and individual-level interpretability of the CatBoost model. To assess the model's generalization performance, bootstrap resampling was performed 10 times. Based on these results, the standard error was improved using machine learning-based enhancement methods, thereby optimizing the model’s robustness and predictive stability.